Safeguarding Data Privacy and Security in Federated Learning Systems

Abstract

This comprehensive study addresses the pressing concerns surrounding data privacy and security in the rapidly expanding domain of federated learning (FL) systems across several domains. This FL, a decentralized machine learning paradigm that enables collaborative model training without data sharing, is scrutinized in-depth. This chapter investigates privacy-preserving strategies tailored to the unique challenges of FL, ensuring the protection of sensitive data. Security vulnerabilities inherent in FL systems are meticulously examined, with a focus on encryption, authentication, and advanced solutions to mitigate security breaches proactively. The study also evaluates established FL frameworks and platforms to assess their effectiveness in safeguarding data. This chapter introduces innovative approaches to enhance data privacy and security throughout the FL process, augmenting existing systems with an emphasis on the healthcare sector. This chapter serves as an essential resource for practitioners, researchers, and policymakers navigating the complex domain of healthcare data privacy and security, fostering confidence and resilience as FL gains prominence. It promotes a climate of trust and dependability in FL systems, shaping the future of collaborative, data-driven endeavors.